Apparatus and method for computer-aided diagnosis

ABSTRACT

An apparatus and method for medical diagnostics includes receiving three-dimensional (3D) volume data of a part of a patient&#39;s body, and generating two-dimensional (2D) slices including cross-sections of the 3D volume data cut from a cross-section cutting direction. The apparatus and the method also determine whether a lesion in each of the 2D slices is benign or malignant and output results indicative thereof, select a number of the 2D slices based on the results, and make a final determination whether the lesion is benign or malignant based on the selected 2D slices.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of prior application Ser.No. 14/469,854, filed on Aug. 27, 2014, which claims the benefit under35 U.S.C. §119(a) of Korean Patent Application No. 10-2013-0127246,filed on Oct. 24, 2013, in the Korean Intellectual Property Office, theentire disclosures of each of which is incorporated herein by referencesfor all purposes.

BACKGROUND

Field

The following description relates to a method and apparatus for medicaldiagnostics using Computer-Aided Diagnosis (CAD).

Description of the Related Art

Computer Aided Diagnosis (CAD) is a system used to create, modify,analyze, or optimize an image design. CAD supports the analyses ofmedical images of a part of a patient's body and diagnoses of a lesionfrom each medical image, which are produced to assist a doctor in makingan accurate diagnosis. Recently, rapid developments in medical equipmenthas prompted introduction of medical devices that process and outputthree-dimensional (3D) images representing inner cross-sections of partsof a patient's body. In addition, many attempts have been made todevelop CAD technologies using 3D images.

A 3D image is an image representing a part of a patient's body in athree-dimensional manner. However, the 3D image does not provide greatvisibility of organs or tissues within volume data. In addition, despiteenormous efforts made to develop CAD techniques using 3D images, whenmaking diagnoses, doctors and other medical experts still depend on andare more comfortable with using two-dimensional (2D) medical images,rather than 3D images.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In accordance with an illustrative configuration, there is provided amethod for medical diagnostics, including receiving three-dimensional(3D) volume data of a part of a patient's body; generatingtwo-dimensional (2D) slices including cross-sections of the 3D volumedata cut from a cross-section cutting direction; determining whether alesion in each of the 2D slices is benign or malignant and outputtingresults indicative thereof; selecting a number of the 2D slices based onthe results; and making a final determination whether the lesion isbenign or malignant based on the selected 2D slices.

The determining may include applying each of the 2D slices to adiagnostic model to determine whether the lesion in each of the 2Dslices is benign or malignant.

The determining may include applying each of the 2D slices to a singlediagnostic model that is generated based on a cross-section cuttingdirection to determine whether the lesion in each of the 2D slices isbenign or malignant.

The determining may include applying the 2D slices to respectivediagnostic models that are generated based on cross-section cuttingdirections of the 2D slices to determine whether the lesion in each ofthe 2D slices is benign or malignant.

Each of the results may include a classification result of the lesion ineach of the 2D slices as either benign or malignant, and a confidencelevel of the classification result.

The generating of the 2D slices may include generating a virtual plane,and generating the 2D slices including cross-sections of the 3D volumedata cut by the virtual plane.

The generating of the virtual plane may include generating the virtualplane by changing coefficient values of a plane equation that representsan arbitrary plane of the 3D volume data.

The generating of the at least one virtual plane may include generatingthe at least one virtual plane by performing principal componentanalysis (PCA) on the 3D volume data.

The generating of the virtual plane may include determining featurepoints having a predetermined feature from among voxels of the 3D volumedata based on values of the voxels, and generating the virtual planebased on a distribution of the feature points by performing the PCA.

The generating of the virtual plane may include calculating a firstprincipal component vector corresponding to an axis in a direction inwhich a change in the 3D volume data is the greatest by performing thePCA, and generating the virtual plane with reference to the firstprincipal component vector.

The generating of the virtual plane may include detecting a massincluded in the 3D volume data based on values of voxels of the 3Dvolume data; and generating the virtual plane based on a distribution ofpoints included in the mass by performing the PCA.

The generating of the virtual plane may include generating the virtualplane based on a user's input information.

In accordance with another illustrative configuration, there is providedan apparatus for medical diagnostics, including a receiver configured toreceive three-dimensional (3D) volume data of a part of a patient'sbody; an image processor configured to generate two-dimensional (2D)slices including cross-sections of the 3D volume data cut from across-section cutting direction; a first diagnoser configured todetermine whether a lesion in each of the 2D slices is benign ormalignant and to output results indicative thereof; a selectorconfigured to select a number of the 2D slices based on the results; anda second diagnoser configured to determine whether the lesion is benignor malignant based on the selected 2D slices.

The first diagnoser may determine whether the lesion in each of the 2Dslices is benign or malignant by applying the 2D slices to a singlediagnostic model.

The first diagnoser may determine whether the lesion in each of the 2Dslices is benign or malignant by applying the 2D slices to a singlediagnostic model generated based on a cross-section cutting direction.

The first diagnoser may determine whether the lesion in each of the 2Dslices is benign or malignant by applying the 2D slices to respectivediagnostic models generated based on cross-section cutting directions ofthe 2D slices.

Each of the results may include a classification result of the lesion ineach of the 2D slices as either benign or malignant and a confidencelevel of the classification result.

The image processor may be further configured to include a virtual planegenerator configured to generate a virtual plane, and a 2D slicegenerator configured to generate 2D slices of cross-sections of the 3Dvolume data cut by the virtual plane.

The virtual plane generator may be further configured to generate thevirtual plane by changing coefficients of a plane equation of anarbitrary plane of the 3D volume data.

The virtual plane generator may be further configured to generate thevirtual plane by performing principal component analysis (PCA) on the 3Dvolume data.

The virtual plane generator may be further configured to determinefeature points with a predetermined feature from among voxels of the 3Dvolume data based on values of the voxels, and generate the virtualplane based on a distribution of the feature points.

The virtual plane generator may be further configured to calculate afirst principal component vector corresponding to an axis in a directiontoward which a greatest change occurs in the 3D volume data byperforming the PCA, and generate the virtual plane based on the firstprincipal component vector.

The virtual plane generator may be further configured to detect a massincluded in the 3D volume data based on values of voxels of the 3Dvolume data, and generate the virtual plane based on a distribution ofpoints included in the mass by performing the PCA.

The virtual plane generator may be further configured to generate thevirtual plane based on a user's input information.

The second diagnoser may be configured to determine whether the lesionis benign or malignant based on a classification result of the lesion ineach of the selected 2D slices.

The first diagnoser may be configured to generate a feature vector usingextracted features of a lesion and apply the feature vector to adiagnostic model to classify the lesion as either benign or malignantand to calculate a confidence level of the classification result.

The selector may bind two or more 2D slices into one group, compareconfidence levels of the 2D slices in the group, and select one or more2D slices in descending order of the confidence levels thereof.

The selector may randomly bind the selected 2D slices into one group,compare confidence levels thereof, and select one or more 2D slices indescending order of the confidence levels thereof.

In accordance with an illustrative configuration, there is provided amethod for medical diagnostics, including receiving three-dimensional(3D) volume data of a part of a patient's body; generatingtwo-dimensional (2D) slices that represent cross-sections of the 3Dvolume data cut from a cross-section cutting direction; generating anintegrated diagnostic model including a combination of diagnostic modelscorresponding to cross-section cutting directions of the 2D slices; anddetermining whether a lesion in each of the 2D slices is benign ormalignant by applying at least one of the 2D slices to the integrateddiagnostic model.

The generating of the integrated diagnostic model may include selecting,from among the diagnostic models, at least one diagnostic modelgenerated based on cross-section cutting directions that are identicalto cross-section cutting directions of the 2D slices; and generating theintegrated diagnostic model by integrating the at least one selecteddiagnostic model.

The generating of the 2D slices may include generating a virtual plane,and generating the 2D slices that represent cross-sections of the 3Dvolume data cut by the virtual plane.

The generating of the virtual plane may include generating the virtualplane by changing coefficients of a plane equation that represents anarbitrary plane in the 3D volume data.

The generating of the virtual plane may include generating the virtualplane by performing principal component analysis (PCA) on the 3D volumedata.

In accordance with another illustrative configuration, there is providedan apparatus for medical diagnostics, including a receiver configured toreceive three-dimensional (3D) volume data of a part of a patient'sbody; an image processor configured to generate two-dimensional (2D)slices including cross-sections of the 3D volume data cut in across-section cutting direction; and a diagnoser configured to generatean integrated diagnostic model including a combination of diagnosticmodels corresponding to cross-section cutting directions of the 2Dslices, and to determine whether a lesion in each of the 2D slices isbenign or malignant by applying at least one of the 2D slices to theintegrated diagnostic model.

The diagnostic models may be generated based on cross-section cuttingdirections that are identical to cross-section cutting directions of the2D slices.

The image processor may be configured to include a virtual planegenerator configured to generate a virtual plane, and a 2D slicegenerator configured to generate the 2D slices that representcross-sections of 3D volume data cut by the virtual plane.

The virtual plane generator may be further configured to generate thevirtual plane by changing coefficients of a plane equation thatrepresents an arbitrary plane in the 3D volume data.

The virtual plane generator may be further configured to generate thevirtual plane by performing principal component analysis (PCA) on the 3Dvolume data.

The image processor may be further configured to perform a principalcomponent analysis (PCA) on the 3D volume data to calculate a firstprincipal component vector corresponding to an axis in a direction inwhich a first greatest change occurs in the 3D volume data and tocalculate a second principal component vector indicating an axis in adirection in which a second greatest change occurs in the 3D volumedata, and configured to calculate a virtual plane including the firstprincipal component vector and the second principal component vector.

The image processor may further include a virtual plane generatorconfigured to calculate a virtual plane having a greatest change of the3D volume data, and generate an additional virtual plane in parallelwith the virtual plane, and a two-dimensional (2D) slice generatorconfigured to extract voxels intersecting the virtual plane among allvoxels in the 3D volume data, and generate each 2D slice by displayingvalues of the extracted voxels as values of pixels on the virtual plane.

Other features and aspects may be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of the embodiments, taken inconjunction with the accompanying drawings in which:

FIG. 1 is a configuration diagram illustrating an apparatus for medicaldiagnostics using Computer-Aided Diagnosis (CAD), according to anembodiment;

FIG. 2 is a configuration diagram illustrating an image processor,according to an embodiment;

FIG. 3 is a configuration diagram illustrating a diagnoser, according toan embodiment;

FIGS. 4A-4C is an example of a full search;

FIGS. 5A-5B and 6A-6C are examples of principal component analysis(PCA);

FIGS. 7 to 9 are examples of a method for selecting a two-dimensional(2D) slice;

FIG. 10 is a flow chart for CAD, according to an embodiment;

FIG. 11 is a flow chart illustrating a method for generating 2D slicesfrom volume data, according to an embodiment; and

FIG. 12 is a flow chart illustrating a method for CAD, according toanother embodiment.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following description is provided to assist the reader in gaining acomprehensive understanding of the methods, apparatuses, and/or systemsdescribed herein. Accordingly, various changes, modifications, andequivalents of the methods, apparatuses, and/or systems described hereinwill be suggested to those of ordinary skill in the art. Also,descriptions of well-known functions and constructions may be omittedfor increased clarity and conciseness.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a,” “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which the present invention belongs. Itwill be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

Hereinafter, embodiments are described in detail with reference toaccompanying drawings.

FIG. 1 is a configuration diagram illustrating an apparatus for medicaldiagnostics using Computer Aided Diagnosis (CAD), according to anembodiment.

Referring to FIG. 1, an apparatus for CAD 100 includes a receiver 110,an image processor 130, and a diagnoser 150. The receiver 110, the imageprocessor 130, and the diagnoser 150 are structural elements.

The receiver 110 receives volume data representing a part of a patient'sbody in a three-dimensional (3D) manner, and transmits the receivedvolume data to the image processor 130. The volume data may be acquiredfrom a three dimensional medical image acquisition device, such as a 3Dultrasound image device using 3D probe, Computed Tomography (CT),Magnetic Resonance Imaging (MRI), or an X-ray capturing device.

For example, in the case of ultrasound images, cross-sectional images ofa specific part of a patient's body are generated by changing a locationand direction of an ultrasound probe placed over the patient's body. Thegenerated cross-sectional images are accumulated to generate volume datarepresenting the specific part of the patient's body in 3D. As such, amethod to generate the volume data by accumulating cross-sectionalimages is called Multiplanar Reconstruction (MPR).

The image processor 130 generates two-dimensional (2D) slices includingcross-sections of the volume data received through the receiver 110. Thevolume data is cut from at least one direction to generate the 2Dslices. The image processor 130 transmits the 2D slices to the diagnoser150. In one example, the image processor 130 generates at least onevirtual plane in 3D space of the volume data, and then generates the 2Dslices including cross-sections of the volume data cut through thegenerated virtual plane. Referring to FIG. 2, the image processor 130includes a virtual plane generator 131 and a 2D slice generator 132.

The virtual plane generator 131 generates at least one virtual planeusing a full search or principal component analysis (PCA).

The virtual plane generator 131 changes an inclination value and centriccoordinates of an arbitrary plane equation to generate all virtualplanes of the volume data, which are generated in 3D space (fullsearch).

For instance, the virtual plane generator 131 generates a virtual planeusing a plane equation like the following Equation 1.

(i−i _(d))+p _(d)(j−j _(d))+q _(d)(k−k _(d))=0   [Equation 1]

Equation 1 is a plane equation that passes through coordinates (id, jd,kd) in 3D space of the volume data and is perpendicular to a vector (1,pd, qd). By changing inclination values pd, qd and centric coordinatesid, jd, kd, the virtual plane generator 131 generates a plane equationof each virtual plane of the volume data that is generated in 3D space.

FIG. 4A-4C illustrate an example of determining a cross-section cuttingdirection by performing a full search. FIG. 4A demonstrates planes ofthe volume data which rotate within a range between −90° and 90° arounda vector being perpendicular to a plane ij and passing through a point(id, jd, kd) in 3D space. FIG. 4A illustrates the planes of the volumedata when an inclination value pd is changed, while other coefficientsof Equation 1 are fixed.

FIG. 4B demonstrates planes which rotates within a range between −90°and 90° around a vector being perpendicular to a plane ik and passingthrough a point (id, jd, kd) in 3D space. In this example, aninclination value qd is changed with other coefficients of Equation 1being fixed.

FIG. 4C demonstrates planes which include a point (id, jd, kd) in 3Dspace, when an inclination value id is changed while other coefficientsof Equation 1 are fixed.

Even in the case where the other coefficients jd and kd of the centriccoordinates are changed, it is possible to generate planes including apoint (id, jd, kd) in the 3D space in a similar way of generating planesas illustrated in FIG. 4C.

In another example, the virtual plane generator 131 generates at leastone virtual plane by performing principal component analysis (PCA) ofthe received volume data.

For instance, the virtual plane generator 131 calculates a firstprincipal component vector corresponding to an axis in a direction inwhich a greatest change occurs in the volume data in 3D space byperforming the PCA on the volume data. The virtual plane generator 131then generates at least one virtual plane based on the first principalcomponent vector. For example, when a first principal component vectorand a second principal component vector are determined through PCA ofthe volume data, the virtual plane generator 131 calculates a virtualplane including the first principal component vector and the secondprincipal component vector. The second principal component vectorindicates an axis in a direction in which a second greatest changeoccurs in the volume data in the 3D space. Because principal componentvectors indicate axes in respective directions in the 3D space, a singleplane may be defined by two of the principal component vectors. Thisplane corresponds to a plane having the greatest change in the volumedata according to the PCA.

Because the PCA is a statistical analysis method, the PCA may notprovide completely accurate results. Therefore, the virtual planegenerator 131 further calculates additional principal component vectors,in addition to the first and second principal component vectors, andthen generates a plurality of planes based on a set of three or moreadditional principal component vectors in a method to generate at leastone virtual plane.

Hereinafter, an example of determining a cross-section cutting directionby performing PCA is described with reference to FIGS. 5A-5B and 6A-6C.

The front plane among all the planes illustrated in FIG. 5A is a planeincluding first and second principal component vectors. The remainingplanes in FIG. 5A are planes that are generated by parallelly moving theplane, including the first principal component vector and the secondprincipal component vector, in a direction of the third principalcomponent vector. The front plane among all the planes illustrated inFIG. 5B is a plane including first and third principal componentvectors. The remaining planes in FIG. 5B are planes that are generatedby parallelly moving the plane, including the first principal componentvector and the third principal component vector, in a direction of thesecond principal component vector.

Furthermore, the virtual plane generator 131 generates at least onevirtual plane according to a change pattern of the volume data in 3Dspace by performing 2D PCA on the volume data. For instance, the virtualplane generator 131 performs 2D PCA on received volume data to calculatea virtual plane having a greatest change of the received volume data in3D space, and generates at least one virtual plane based on thecalculated virtual plane. In addition, the virtual plane generator 131generates an additional virtual plane by moving the previously generatedvirtual plane in parallel, so that the additionally generated virtualplane is in parallel with the previously generated virtual plane.

Referring to FIG. 6A, the virtual plane generator 131 detects a massincluded in the volume data based on voxel values of the volume data,and calculates a principal component vector from a distribution ofpoints included in the mass in a 3D space. The mass is an objectincluded in the volume data. When the volume data includes a mass, voxelvalues may be significantly changed in a boundary of the mass. However,anatomical tissues are not homogeneous, and boundaries of the anatomicaltissues are not clearly defined in an image in most cases. In addition,information regarding a form or an image characteristic of apredetermined tissue that a medical expert desires to diagnose may benecessary.

There are various methods of segmenting a mass in volume data, such as alevel set method. The level set method (LSM) is a numerical techniquefor tracking interfaces and shapes. One of the many advantages of thelevel set method is that one can perform numerical computationsinvolving curves and surfaces on a fixed Cartesian grid without havingto parameterize these objects. Also, the level set method makes enablesto follow shapes that change topology, for example when a shape splitsin two, develops holes, or the reverse of these operations. For example,the virtual plane generator 131 segments the mass in volume data basedon voxel values of the volume data using the level set method.

Referring to FIG. 6B, the virtual plane generator 131 identifies featurepoints, including a feature, such as a form or an image characteristicof a predetermined tissue from among the voxels of the volume data basedon the values of the voxels. The virtual plane generator 131 calculatesa principal component vector from a distribution of the identifiedpoints in a 3D space.

Referring to FIG. 6C, the virtual plane generator 131 calculates afeature of any one of the voxels of the volume data using a voxel sethaving a predetermined size. A voxel is located at the center of thevoxel set. In one example, the virtual plane generator 131 determines amean brightness value of voxels forming the voxel set as a feature ofthe center voxel of the voxel set. In another example, the virtual planegenerator 131 determines a variance of brightness values of voxelsforming the voxel set as the feature of the center voxel of the voxelset.

Also, a method in which the virtual plane generator 131 generates avirtual plane is not limited to the above-described examples, and othervarious methods may be applied. For example, the virtual plane generator131 generates at least one virtual plane based on a user's inputinformation. The virtual plane generator 131 generates at least onevirtual plane with reference to a cutting direction input by a userthrough a user interface.

The two-dimensional (2D) slice generator 132 generates 2D slicesincluding cross sections of volume data cut from the virtual planegenerated at the virtual plane generator 131. For instance, the 2D slicegenerator 132 extracts voxels intersecting the virtual plane generatedat the virtual plane generator 131 among all the voxels in the volumedata. Then, the 2D slice generator 132 generates each 2D slice bydisplaying values of the extracted voxels as values of pixels on thevirtual plane.

An image having a resolution sufficient for a medical practitioner toperform diagnosis may not be provided using only the voxels crossed bythe virtual plane generated at the virtual plane generator 131 fromamong the voxels of the volume data. Accordingly, in order to improvethe resolution sufficient for a medical practitioner to performdiagnosis using the values of the voxels of the volume data, the 2Dslice generator 132 interpolates additional pixels of a 2D image otherthan the pixels of the 217 image corresponding to the extracted voxels.The 2D slice generator 132 generates a single 2D image by merging valuesof the pixels of the 2D image corresponding to the extracted voxels andthe interpolated values of the additional pixels of the 2D image. Inthis manner, an image having a resolution sufficient for a medicalexpert to perform a diagnosis is generated through this interpolation.

The diagnoser 150 performs diagnosis on each of the 2D slices generatedat the 2D slice generator 110. The diagnoser 150 is a structuralprocessor, machine, or a structural intelligent central elementconfigured to make a determination as to whether a lesion in each of the2D slices is benign or malignant. The determination may include aclassification result of a lesion as either benign or malignant, and aconfidence level of the classification result. The diagnoser 150 mayselect some of the 2D slices based on results of the determination, andthen make a final determination as to whether a lesion in the volumedata is benign or malignant based on results of determination relativeto the selected 2D slices.

Referring to FIG. 3, the diagnoser 150 may include a first diagnoser151, a selection unit 152 and a second diagnoser 153. The firstdiagnoser 151 determines whether a lesion in each of a plurality of 2Dslices generated by the image processor 130 is benign or malignant. Forinstance, the first diagnoser 151 segments a lesion such that a locationof the lesion from each of the 2D slices is detected, and an accuratecontour of the lesion is displayed based the detected location. Forexample, the first diagnoser 151 segments a lesion using varioussegmentation schemes including a region growing algorithm, a level setalgorithm, and a genetic algorithm.

In addition, the first diagnoser 151 is configured to extract featuresof the segmented lesion to make a determination as to whether aparticular segment of the lesion shown in each of the 2D slices isbenign or malignant. The features of the segmented lesion aremorphological features, such as contour shape, margin, direction,speculation, and micro-lobulation of a lesion. In another example, thefeatures of the segmented lesion are the image's unique characteristics.In the case of an ultrasound image, features of a lesion may include anecho pattern and posterior acoustic shadow.

The first diagnoser 151 is configured to generate a feature vector usingextracted features of a lesion and apply the feature vector to at leastone diagnostic model to thereby classify a lesion as either benign ormalignant. The first diagnoser 151 is also configured to calculate aconfidence level of the classification result. In one illustrativeexample, the operation of generating a diagnostic model, the operationof classifying a lesion as either benign or malignant using a diagnosticmodel, and the operation of calculating a confidence level of theclassification result are performed through machine learning. Forexample, various kinds of algorithms, including neural network, Bayesianclassifier, multi-layer perceptron, and Support Vector Machine (SVM),may be utilized for machine learning.

In one illustrative configuration, a diagnostic model is generated asfollows: features of a lesion in each 2D slice generated from previouslyacquired volume data sets are extracted; a feature vector is generatedusing the extracted features; the lesion is classified as either benignor malignant with respect to the feature vector; and results of theclassification are used as training data for generating a diagnosticmodel.

The first diagnoser 151 is configured to diagnose each of the 2D slicesgenerated by the image processor 130 using at least one diagnosticmodel.

In one example, in the case of liver, a cyst, or a hemangioma, across-section cutting direction does not make a big difference in theform of a lesion. Thus, even in the case where a diagnostic model,generated with reference to a specific cross-section cutting direction,is used to perform diagnosis on a 2D slice generated referencing adifferent cross-section cutting direction, it does not cause asignificant difference to a diagnosis result. Accordingly, the firstdiagnoser 151 performs diagnosis by generating a single diagnostic modelthrough machine learning using 2D slices, which are generated astraining data by cutting previously acquired volume data from apredetermined cross-section cutting direction. The first diagnoser 151then applies each of the generated 2D slices to the diagnostic model.

In another example, in the case of breast or lung, a lesion may have adifferent form in each 2D slice according to a cross-section cuttingdirection. Thus, a diagnosis result of a 2D slice may be dependent on across-section cutting direction. Accordingly, the first diagnoser 151generates diagnostic models according to different cross-section cuttingdirections of 2D slices generated at the image processor 130. The firstdiagnoser 151 then perform diagnosis by applying the 2D slices to thediagnostic models, respectively.

For instance, the first diagnoser 151 generates diagnostic models using2D slices as training data, which includes cross sections ofpreviously-acquired volume data cut from different directions. Then, thefirst diagnoser 151 performs diagnosis on each of the 2D slices byapplying the 2D slices to respective diagnostic models, which aregenerated based on cutting directions that are identical to those of the2D slices.

Based on results of the determinations made by the first diagnoser 151as to the 2D slices, the selector 152 selects some of the 2D slices. Forinstance, the selector 152 selects some of the 2D slices based on aclassification result of a lesion in each of the 2D slices as eitherbenign or malignant, based on a confidence level of the classificationresult, or based on both. At this point, the 2D slice is selected usingvarious selection techniques, such as ranking-based selection,tournament selection, roulette wheel, and probability sampling.

Referring to FIG. 7, the selector 152 is configured to select k numberof 2D slices in descending order of confidence levels thereof. In theexample shown in FIG. 7, a specific numerical value (e.g.,cross-sections 1, 3, 4, and 5) assigned to a cross section indicatesthat a specific cross-section is generated from a specific cuttingdirection. Slices 5, 7, 8, and 9 are classified as benign, and slices 1and 12 are classified as malignant. The selector 152 aligns the 2Dslices generated by the image processor 130 in descending order ofconfidence levels of classification results thereof, and selects knumber of the 2D slices in descending order of the confidence levels.

Referring to FIG. 8, the selector 152 is further configured to extract apredetermined number of 2D slices of volume data cut from differentcross-section cutting directions, and select k number of 2D slices fromthe extracted 2D slices. In the example of FIG. 8, when N number ofcross-section cutting directions is used to generate 2D slices, theselector 152 randomly selects k/N number of 2D slices with higherconfidence levels to thereby select k number of 2D slices.

Referring to FIG. 9, the selector 152 binds two or more 2D slices intoone group, compares confidence levels of the 2D slices in the group, andselects one or more 2D slices in descending order of the confidencelevels thereof. The selector 142 also randomly binds the selected 2Dslices into one group, compares confidence levels thereof, and selectsone or more 2D slices in descending order of the confidence levelsthereof. The selector 152 may repeat the above process until k number of2D slices is selected.

The second diagnoser 153 is configured to make a final determination asto whether a lesion in each of the 2D slices is benign or malignantbased on results of the determination relative to the k number of 2Dslices selected by the selector 152. For instance, the second diagnoser153 makes a final determination as to whether a lesion in each of the knumber of 2D slices is benign or malignant using a voting method, whichincludes majority voting, a statistical method, which includes average,and an ensemble method which includes Adaboost, Bayes optimalclassifier, and bootstrap aggregating.

In one example, in the case where two out of five 2D slices selected bythe selector 152 are classified as malignant and other three 2D slicesare classified as benign, the second diagnoser 153 makes a finaldetermination that a lesion is benign.

In another example, in a case where two out of five 2D slices selectedby the selector 152 are classified as malignant with confidence levelsof 60% and 75%, respectively, while three other 2D slices are classifiedas benign with confidence levels of 60%, 80% and 85%, respectively, anaverage confidence level relative to malignancy is 67.5% and an averageconfidence level relative to benignity is 75%. Because a confidencelevel of benignity is greater than that of malignancy, the seconddiagnoser 153 makes a final determination that a lesion is benign.

Again, referring to FIG. 2, the diagnoser 150 selects one or morediagnostic models from diagnostic models, and determines benignity ormalignancy of a lesion using an integrated diagnostic model, which is acombination of the selected diagnostic models, as contrary to theexample shown in FIG. 3. For example, the diagnoser 150 integratesdiagnostic models, each diagnostic model generated according to adifferent cross-section cutting direction, into one diagnostic model,and applies 2D slices generated by the image processor 130 to theintegrated diagnostic model in order to deduct a final diagnosis result.

For example, the diagnoser 150 selects from diagnostic models one ormore diagnostic models, which are generated based on cutting directionsthat are identical to those of the 2D slices generated at the imageprocessor 130. The diagnoser 150 integrates the selected diagnosticmodels into one integrated diagnostic model. The diagnoser 150 generatesa feature vector by calculating feature values of each of the 2D slices,and applies the feature vector to the integrated diagnostic model todetermine whether a lesion in each of the 2D slices is benign ormalignant.

The receiver 110, the image processor 130, the virtual plane generator131, the slice generator 132, the diagnoser 150, the first and seconddiagnosers 151 and 153, and the selector 152 described herein may beimplemented using hardware components. For example, controllers,generators, microphones, amplifiers, band-pass filters, audio to digitalconvertors, and processing devices. A processing device may beimplemented using one or more general-purpose or special purposecomputers, such as, for example, a processor, a controller and anarithmetic logic unit, a digital signal processor, a microcomputer, afield programmable array, a programmable logic unit, a microprocessor orany other device capable of responding to and executing instructions ina defined manner. The processing device may run an operating system (OS)and one or more software applications that run on the OS. The processingdevice also may access, store, manipulate, process, and create data inresponse to execution of the software. For purpose of simplicity, thedescription of a processing device is used as singular; however, oneskilled in the art will appreciated that a processing device may includemultiple processing elements and multiple types of processing elements.For example, a processing device may include multiple processors or aprocessor and a controller. In addition, different processingconfigurations are possible, such a parallel processors.

FIG. 10 is a flow chart illustrating a method for CAD, according to anembodiment.

Referring to FIG. 10, at operation 1010, the method receives volume datathrough the receiver 110. At operation 1020, through the image processor130, the method generates 2D slices including cross-sections of thereceived volume data cut by at least one cutting direction.

At operation 1030, using the diagnoser 150, the method determineswhether a lesion in each of the 2D slices is benign or malignant. In oneexample, the method uses the diagnoser 150 to apply the 2D slicesgenerated at operation 1020 to a single diagnostic model generated basedon a particular cross-section cutting direction in order to classify alesion in each of the 2D slices as either benign or malignant, andcalculate a confidence level of the classification result.

In another example, the method uses the diagnoser 150 to apply each ofthe 2D slices, generated at operation 1020, to diagnostic modelsgenerated based on different cross-section cutting directions in orderto classify a lesion in each of the 2D slices as either benign ormalignant, and calculate a confidence level of the classificationresult. In one example, the method uses the diagnoser 150 to apply theplurality of 2D slices to respective diagnostic model which aregenerated based on cutting directions, which are identical to those ofthe 2D slices among the diagnostic models. At operation 1040, the methoduses the diagnoser 150 to select some of the 2D slices based on resultsof the determinations thereof. At this point, the 2D slices may beselected using various selection techniques, such as ranking-basedselection, tournament selection, roulette-wheel selection, andprobability sampling.

When some of the 2D slices are selected, at operation 1050, the methoduses the diagnoser 150 to make a final determination as to whether alesion in each of the 2D slices is benign or malignant based on resultsof the determination relative to the selected 2D slices. The finaldetermination may be made using a voting method including majorityvoting, a statistical method including average, and an ensemble methodincluding Adaboost, Bayes optimal classifier, and bootstrap aggregating.

FIG. 11 is a flow chart illustrating a method for generating 2D slicesfrom volume data, according to an embodiment.

Referring to FIG. 11, at operation 1110, the method receives volume datafrom the receiver 110, and generates at least one virtual plane from thereceived volume data in 1110. The method generates at least one virtualplane by performing full search or PCA. However, the present disclosureis not limited thereto, and other various methods may be applied. Forexample, the method may generate at least one virtual plane based on auser's input information.

In the case where at least one virtual plane is generated, at operation1120, the method generates 2D slices including cross sections of volumedata cut by the generated virtual plane.

FIG. 12 is a flow chart illustrating a method for CAD, according toanother embodiment. Referring to FIG. 12, at operation 1210, the methodreceives volume data through the receiver 110. At operation 1220, themethod uses the image processor 130 to generate 2D slices includingcross-sections of the received volume data cut from at least onecross-section cutting direction. The 2D slices may be generated usingthe above-mentioned methods.

At operation 1230, the method uses the diagnoser 150 to generate anintegrated diagnostic model by integrating diagnostic modelscorresponding to cross-section cutting directions of the 2D slices intoone integrated diagnostic model. For instance, the method uses thediagnoser 150 to select one or more diagnostic models, which aregenerated based on cross-section cutting directions that are identicalto those of the 2D slices. Then, the method generates an integrateddiagnostic model by integrating the selected diagnostic models.

At operation 1240, the method uses the diagnoser 150 to apply the 2Dslices to the integrated diagnostic model to make a determination as towhether a lesion in each of the 2D slices is benign or malignant.

It is to be understood that in the embodiment of the present invention,the operations in FIGS. 10-12 are performed in the sequence and manneras shown although the order of some operations and the like may bechanged without departing from the spirit and scope of the describedconfigurations. In accordance with an illustrative example, a computerprogram embodied on a non-transitory computer-readable medium may alsobe provided, encoding instructions to perform at least the methoddescribed in FIGS. 10-12.

Program instructions to perform a method described in FIGS. 10-12, orone or more operations thereof, may be recorded, stored, or fixed in oneor more non-transitory computer-readable storage media. The programinstructions may be implemented by a computer. For example, the computermay cause a processor to execute the program instructions. The media mayinclude, alone or in combination with the program instructions, datafiles, data structures, and the like. Examples of non-transitorycomputer-readable media include magnetic media, such as hard disks,floppy disks, and magnetic tape; optical media such as CD ROM disks andDVDs; magneto-optical media, such as optical disks; and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory (ROM), random access memory (RAM), flashmemory, and the like. Examples of program instructions include machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter. Theprogram instructions, that is, software, may be distributed over networkcoupled computer systems so that the software is stored and executed ina distributed fashion. For example, the software and data may be storedby one or more computer readable recording mediums. Also, functionalprograms, codes, and code segments for accomplishing the exampleembodiments disclosed herein may be easily construed by programmersskilled in the art to which the embodiments pertain based on and usingthe flow diagrams and block diagrams of the figures and theircorresponding descriptions as provided herein.

A number of examples have been described above. Nevertheless, it will beunderstood that various modifications may be made. For example, suitableresults may be achieved if the described techniques are performed in adifferent order and/or if components in a described system,architecture, device, or circuit are combined in a different mannerand/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

What is claimed is:
 1. A method for medical diagnostics, comprising:receiving three-dimensional (3D) volume data of at least a part of anobject; generating two-dimensional (2D) slices including cross-sectionsof the 3D volume data based on a cross-section cutting direction;classifying a lesion in at least two of the generated 2D slices;calculating confidence levels of results of the classification;selecting a plurality of the at least two 2D slices based on theconfidence levels of the results of the classification; andre-classifying a lesion included in the 3D volume data based on at leastone of the classification results of the selected 2D slices or theconfidence levels of the classification results of the selected 2Dslices.
 2. The method of claim 1, further comprising: outputting there-classification result.
 3. The method of claim 1, wherein theclassifying of the lesion comprises applying the at least two 2D slicesto a diagnostic model to classify the lesion in the at least two 2Dslices.
 4. The method of claim 3, wherein the classifying of the lesioncomprises applying the at least two 2D slices to a single diagnosticmodel that is generated based on the cross-section cutting direction. 5.The method of claim 3, wherein the classifying of the lesion comprisesapplying the at least two 2D slices to respective diagnostic models thatare generated based on cross-section cutting directions of the at leasttwo 2D slices.
 6. The method of claim 1, wherein the generating of the2D slices comprises: generating at least one virtual plane; andgenerating the 2D slices including cross-sections of the 3D volume datacut by the virtual plane.
 7. The method of claim 6, wherein thegenerating of the at least one virtual plane comprises at least one of:generating the at least one virtual plane by changing coefficient valuesof a plane equation that represents an arbitrary plane of the 3D volumedata; generating the at least one virtual plane by performing principalcomponent analysis (PCA) on the 3D volume data; or generating the atleast one virtual plane based on a user's input information.
 8. Themethod of claim 7, wherein the generating of the at least one virtualplane further comprises at least one of: determining feature pointshaving a predetermined feature from among voxels of the 3D volume databased on values of the voxels, and generating the at least one virtualplane based on a distribution of the feature points by performing thePCA; calculating a first principal component vector corresponding to anaxis in a direction in which a change in the 3D volume data is thegreatest by performing the PCA, and generating the at least one virtualplane with reference to the first principal component vector; ordetecting a mass included in the 3D volume data based on values ofvoxels of the 3D volume data, and generating the at least one virtualplane based on a distribution of points included in the mass byperforming the PCA.
 9. An apparatus for medical diagnostics, comprising:a receiver configured to receive three-dimensional (3D) volume data ofat least a part of an object; and at least one processor configured to:generate two-dimensional (2D) slices including cross-sections of the 3Dvolume data based on a cross-section cutting direction, classify alesion in at least two of the generated 2D slices, calculate confidencelevels of the results of the classification, select a plurality of theat least two 2D slices based on the confidence levels of the results ofthe classification, and re-classify a lesion included in the 3D volumedata based on at least one of the classification results of the selected2D slices or the confidence levels of the classification results of theselected 2D slices.
 10. The apparatus of claim 9, wherein the at leastone processor is further configured to output the re-classificationresult.
 11. The apparatus of claim 9, wherein the at least one processoris further configured to classify the lesion in the at least two 2Dslices by applying the at least two 2D slices to a diagnostic model. 12.The apparatus of claim 11, wherein the at least one processor is furtherconfigured to classify the lesion in the at least two 2D slices byapplying the at least two 2D slices to a single diagnostic modelgenerated based on a cross-section cutting direction.
 13. The apparatusof claim 11, wherein the at least one processor is further configured toclassify the lesion in the at least two 2D slices by applying the atleast two 2D slices to respective diagnostic models generated based oncross-section cutting directions of the at least two 2D slices.
 14. Theapparatus of claim 9, wherein the at least one processor is furtherconfigured to: generate at least one virtual plane, and generate 2Dslices of cross-sections of the 3D volume data cut by the virtual plane.15. The apparatus of claim 14, wherein the at least one processor isfurther configured to generate the at least one virtual plane bychanging coefficients of a plane equation of an arbitrary plane of the3D volume data by at least one of: performing principal componentanalysis (PCA) on the 3D volume data, or based on a user's inputinformation.
 16. The apparatus of claim 15, wherein the at least oneprocessor is further configured to: determine feature points with apredetermined feature from among voxels of the 3D volume data based onvalues of the voxels, and generate the at least one virtual plane basedon a distribution of the feature points, calculate a first principalcomponent vector corresponding to an axis in a direction toward which agreatest change occurs in the 3D volume data by performing the PCA, andgenerate the at least one virtual plane based on the first principalcomponent vector, or detect a mass included in the 3D volume data basedon values of voxels of the 3D volume data, and generate the at least onevirtual plane based on a distribution of points included in the mass byperforming the PCA.
 17. The apparatus of claim 9, wherein the at leastone processor is further configured to: generate a feature vector usingextracted features of the lesion in the at least two 2D slices, andapply the feature vector to a diagnostic model to classify the lesion inthe at least two 2D slices.
 18. The apparatus of claim 9, wherein the atleast one processor is further configured to: bind two or more 2D slicesinto one group, compare confidence levels of the 2D slices in the group,and select one or more 2D slices in descending order of the confidencelevels thereof.
 19. The apparatus of claim 9, wherein the at least oneprocessor is further configured to: randomly bind the selected 2D slicesinto one group, compare confidence levels thereof, and select one ormore 2D slices in descending order of the confidence levels thereof. 20.A computer program product comprising a computer readable storage mediumhaving a computer readable program stored therein, wherein the computerreadable program, when executed on at least one processor, configuresthe at least one processor to: receive three-dimensional (3D) volumedata of at least a part of an object; generate two-dimensional (2D)slices including cross-sections of the 3D volume data based on across-section cutting direction; classify a lesion in at least two ofthe generated 2D slices; calculate confidence levels of results of theclassification; select a plurality of the at least two 2D slices basedon the confidence levels of the results of the classification; andre-classify a lesion included in the 3D volume data based on at leastone of the classification results of the selected 2D slices or theconfidence levels of the classification results of the selected 2Dslices.